Abstract
BACKGROUND AND AIMS: Rapid, large-scale monitoring is critical to understanding spatiotemporal plant stress dynamics, but current physiological stress markers are costly, destructive and time-consuming. This study aimed to evaluate the potential of machine learning to non-destructively predict leaf betalains - yellow to reddish pigments unique to Caryophyllales species - for the first time, and to explore intra-individual variation in betalains in a clonal species and its role in responding to stressful periods. METHODS: We characterized the betalainic profile of an invasive clonal plant for the first time, Carpobrotus edulis (the cape fig), via high-performance liquid chromatography. We measured multiple stress markers over a year, including betalain content using our optimized method, where the species is spreading. Additionally, 3735 digital images at the leaf level were taken. Machine learning regression algorithms were trained to predict betalain accumulation from digital images, outperforming classic spectroradiometer measurements. KEY RESULTS: Betalain content increased sharply in non-reproductive ramets during extreme abiotic conditions in summer and during senescence in reproductive ramets. The stress markers revealed a strong intra-individual functional mosaic, underscoring the importance of spatiotemporal dimensions in stress tolerance. CONCLUSIONS: We developed a scalable, non-destructive tool for betalain research that integrates digital imaging with machine learning. This approach opens new possibilities for understanding spatiotemporal stress responses, particularly in clonal plant systems, using artificial intelligence.